From Morphogenesis to Morphospace

Computational Biological Visualization, Quantitative Image Analysis & Exploring Complex Data

Analyses of multivariate, multi-scale nonlinear phenomena require new tools and conceptual approaches. How do we help viewers interpret and appreciate irregular, asymmetric patterns that occur in biological images and complex networks? Image analysis and graph theory that allow users to test hypotheses about causal mechanisms about how images and networks are formed are particularly powerful. In addition, they also help reduce the topological dimension of data so that subpatterns can be identified. However, image and network analyses without a mathematical model usually means that basic descriptive statistics are collected that re-describe properties of an image without actually testing a causal hypothesis. In this seminar, Dr. John Jungck will illustrate four models that instantiate the paleontologist Dolph Seilacher’s dictum: “I wouldn’t have seen it, if I hadn’t believed it.” Thus, mathematics is a lens that helps us see from a perspective. In particular, Jungck believes that if the mathematics is easily accessible to biologists, then they are not only apt to use it, but also to understand how it helps them with their work. Jungck will demonstrate five software applications that have been developed based upon geometry, spatial statistics, graph theory, cellular automata, and fractals to problems of differentiating normal from abnormal cell divisions, adaptive geometry of trees, food webs, root growth, biofilms, pattern formation, species transformations, and morphospace. In particular, he will illustrate how three DRYAD data sets from NESCent have been used to build interactive exploratory problem spaces.